System and method for increasing accuracy of searches based on communication network

ABSTRACT

Disclosed are systems, methods and computer-readable media for using a local communication network to generate a speech model. The method includes retrieving for an individual a list of numbers in a calling history, identifying a local neighborhood associated with each number in the calling history, truncating the local neighborhood associated with each number based on the at least one parameter, retrieving a local communication network associated with each number in the calling history and each phone number in the local neighborhood, and creating a language model for the individual based on the retrieved local communication network. The generated language model may be used for improved automatic speech recognition for audible searches as well as other modules in a spoken dialog system.

PRIORITY INFORMATION

The present application is a continuation of U.S. patent applicationSer. No. 11/931,830, filed Oct. 31, 2007, the content of which isincluded herewith in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to spoken dialog systems and morespecifically to a system and method of using a local communicationnetworks to generate more accurate speech recognition models which maybe deployed in a spoken dialog system.

2. Introduction

Search for information on the web using a mobile phone is an area thathas been expanding significantly over the last several of years. This isat least in part to the exponential growth in the number of mobile usersand their willingness to pay for data access. There are over 15 billionsearch queries made on the web annually while the number of text searchqueries over the phone is fewer than 30 million per year. However, thereis an increasing trend especially among younger generation to use mobilephones as a primary medium for an information search. Mobile informationsearch is a major growth area that continues to expand exponentiallyyear-after-year and is an important opportunity for new business remedy.

There are two main challenges in mobile information access. First, thesmall screen size limits the amount of information output, and second,the lack of a keyboard poses difficulty when typing words or phrases.This is also applicable in small devices such as Blackberry or Palm Triodevices where the keyboard is small and relatively difficult to use,especially while on the go. What is needed in the art is an improvedmechanism to enable users to perform search through providing searchterms audibly over a telephone.

SUMMARY

Additional features and advantages of the invention will be set forth inthe description which follows, and in part will be obvious from thedescription, or may be learned by practice of the invention. Thefeatures and advantages of the invention may be realized and obtained bymeans of the instruments and combinations particularly pointed out inthe appended claims. These and other features of the present inventionwill become more fully apparent from the following description andappended claims, or may be learned by the practice of the invention asset forth herein.

Disclosed are systems, methods and a computer-readable medium thataddress the issue identified above by providing a smarter speechinterface for telephone or audible searches by creating personalized andadaptive models for each user (or group of users) based on the conceptof a “local communication network”. Phone users can search forinformation easily using speech input and obtain superior searchretrieval accuracy using the principles of the invention. High accuracyand reliability of search will not only increase the number of usersdepending on speech input as the primary means for mobile informationaccess or telephone information access but it could also provideincreased revenue. A method aspect of the invention includes using alocal communication network to generate a speech recognition model. Themethod includes retrieving from an individual a list of numbers in acalling history, identifying a local neighborhood associated with eachnumber in the calling history, truncating the local neighborhoodassociated with each number based on at least one parameter, retrievinga local communication network associated with each number in the callinghistory and each phone number in the local neighborhood, and creating alanguage model for the individual based on the retrieved localcommunication network.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the invention can be obtained, a moreparticular description of the invention briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the invention and are not thereforeto be considered to be limiting of its scope, the invention will bedescribed and explained with additional specificity and detail throughthe use of the accompanying drawings in which:

FIG. 1 illustrates the basic system embodiment;

FIG. 2 illustrates the basic components of a spoken dialog system;

FIG. 3 illustrates the basic components according to an aspect of theinvention;

FIG. 4 illustrates further basic modules that may be utilized inconnection with the invention; and

FIG. 5 illustrates a method embodiment of the invention.

DETAILED DESCRIPTION

Various embodiments of the invention are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the invention.

With reference to FIG. 1, an exemplary system includes a general-purposecomputing device 100, including a processing unit (CPU) 120 and a systembus 110 that couples various system components including the systemmemory such as read only memory (ROM) 140 and random access memory (RAM)150 to the processing unit 120. Other system memory 130 may be availablefor use as well. It can be appreciated that the invention may operate ona computing device with more than one CPU 120 or on a group or clusterof computing devices networked together to provide greater processingcapability. The system bus 110 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Abasic input/output (BIOS) stored in ROM 140 or the like, may provide thebasic routine that helps to transfer information between elements withinthe computing device 100, such as during start-up. The computing device100 further includes storage devices such as a hard disk drive 160, amagnetic disk drive, an optical disk drive, tape drive or the like. Thestorage device 160 is connected to the system bus 110 by a driveinterface. The drives and the associated computer readable media providenonvolatile storage of computer readable instructions, data structures,program modules and other data for the computing device 100. The basiccomponents are known to those of skill in the art and appropriatevariations are contemplated depending on the type of device, such aswhether the device is a small, handheld computing device, a desktopcomputer, or a computer server.

Although the exemplary environment described herein employs the harddisk, it should be appreciated by those skilled in the art that othertypes of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, digital versatile disks, cartridges, random access memories(RAMs), read only memory (ROM), a cable or wireless signal containing abit stream and the like, may also be used in the exemplary operatingenvironment.

To enable user interaction with the computing device 100, an inputdevice 190 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. The deviceoutput 170 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems enable a user to provide multiple types of input to communicatewith the computing device 100. The communications interface 180generally governs and manages the user input and system output. There isno restriction on the invention operating on any particular hardwarearrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

For clarity of explanation, the illustrative system embodiment ispresented as comprising individual functional blocks (includingfunctional blocks labeled as a “processor”). The functions these blocksrepresent may be provided through the use of either shared or dedicatedhardware, including, but not limited to, hardware capable of executingsoftware. For example the functions of one or more processors presentedin FIG. 1 may be provided by a single shared processor or multipleprocessors. (Use of the term “processor” should not be construed torefer exclusively to hardware capable of executing software.)Illustrative embodiments may comprise microprocessor and/or digitalsignal processor (DSP) hardware, read-only memory (ROM) for storingsoftware performing the operations discussed below, and random accessmemory (RAM) for storing results. Very large scale integration (VLSI)hardware embodiments, as well as custom VLSI circuitry in combinationwith a general purpose DSP circuit, may also be provided.

Spoken dialog systems aim to identify intents of humans, expressed innatural language, and take actions accordingly, to satisfy theirrequests. FIG. 2 is a functional block diagram of an exemplary naturallanguage spoken dialog system 200. Natural language spoken dialog system200 may include an automatic speech recognition (ASR) module 202, aspoken language understanding (SLU) module 204, a dialog management (DM)module 206, a spoken language generation (SLG) module 208, and atext-to-speech (TTS) module 210. The TTS module may be any type ofspeech output module. For example, it may be a module wherein one of aplurality of prerecorded speech segments is selected and played to auser. Thus, the TTS module represents any type of speech output. Thepresent invention focuses on innovations related to the dialogmanagement module 106 and may also relate to other components of thedialog system.

ASR module 202 may analyze speech input and may provide a transcriptionof the speech input as output. SLU module 204 may receive thetranscribed input and may use a natural language understanding model toanalyze the group of words that are included in the transcribed input toderive a meaning from the input. The role of DM module 206 is tointeract in a natural way and help the user to achieve the task that thesystem is designed to support. DM module 206 may receive the meaning ofthe speech input from SLU module 204 and may determine an action, suchas, for example, providing a response, based on the input. SLG module208 may generate a transcription of one or more words in response to theaction provided by DM 206. TTS module 210 may receive the transcriptionas input and may provide generated audible speech as output based on thetranscribed speech.

Thus, the modules of system 200 may recognize speech input, such asspeech utterances, may transcribe the speech input, may identify (orunderstand) the meaning of the transcribed speech, may determine anappropriate response to the speech input, may generate text of theappropriate response and from that text, may generate audible “speech”from system 200, which the user then hears. In this manner, the user cancarry on a natural language dialog with system 200. Those of ordinaryskill in the art will understand the programming languages and means forgenerating and training ASR module 202 or any of the other modules inthe spoken dialog system. Further, the modules of system 200 may operateindependent of a full dialog system. For example, a computing devicesuch as a smartphone (or any processing device having a phonecapability) may have an ASR module wherein a user may say “call mom” andthe smartphone may act on the instruction without a “spoken dialog.”

The principles disclosed herein may be employed to use data andknowledge from the “local communication network” to create customizedand personalized speech recognition models that would result in highaccuracy information speech searches. Many companies including Google®,Yahoo®, AT&T, Microsoft, among others, are engaged in activities toallow users to say a few words or phrases that can be used forinformation search. The difficulty with using speech input for mobile oraudible information search is poor accuracy of the speech recognizer(202) when presented with open vocabulary. As a reference point,state-of-the-art recognizers can achieve about 70% word recognitionaccuracy for 100,000 words with perplexity (the average number ofdifferent words spoken following a given word) of around 60-80. However,the perplexity when dealing with information search is significantlymore than 80 and can reach in the order of thousands. This reflects thenumerous variability in the keywords that different users apply whenperforming mobile information search. For perplexity figures at thatlevel, it is expected to open vocabulary information search using speechinput that can only reach levels of 20-30% word accuracy. At theseaccuracy levels, speech information search is quite impractical. As aresult of this, there have been no commercial capabilities forgeneral-purpose mobile information search using speech input. Instead,the previous best technology available could only support limitedsearch, such as directory assistance with automation levels in theregion of 40-50%.

FIG. 3 illustrates some basic understanding of a possible definition ofa local communication network. First a calling history 302 is associatedwith a user, a phone device, a business or any other organized groupingof a calling history. Each phone number in the calling history may havean associated local neighborhood 304-1, 304-2, . . . 304-n. The localneighborhood may be defined as a list of other numbers which called orare called by the given number in the calling history, and may entailother information such as a billing history, location-based information(i.e., an identified location of a user when a given number is called orcalled the given number) and so forth. This list is typically receivedbased on a calling history of other individuals. Associated with eachnumber (or piece of data) in the local neighborhood, is a localcommunication network (LCN) LCN-1 (306-1), LCN-2 (306-2), . . . LCN-n(306-n) represents further corresponding data associated with each localneighborhood. For example, each number or piece of data in the localneighborhood will have a corresponding entity, business name, personname, or other data associated with the number. Gathering thisinformation may be accomplished using a search or database retrievalfrom a stored directory. In one aspect, the list of phone numbers in thelocal neighborhood and corresponding entity and other data associatedwith each number may be referred to as the overall LCN 300 for anindividual or other entity. As an example, the LCN may represent a listof potential entities that a user calls or would most likely call. Thus,the listing of pizza, restaurants, various shops, insurance, business,family members and so forth are all identified within this overall localcommunication network for an individual, device and so forth.

FIG. 4 illustrates another basic aspect of the use of the invention.Utterance 402 may be received by a spoken dialog system which mayperform some preprocessing such as user identification 404. In thisregard, an example would be to identify the user according to knownspeaker identification techniques and based on the identification of theuser, retrieving from a database 406 an identification of a speechrecognition model or other personal speech models that may be thendynamically employed using an ASR 406 system. The utterance is theninterpreted wherein the speech is converted into text and then passed onfor further processing to other modules in a spoken dialog system suchas a spoken language module 204. In this manner, the system may be ableto utilize an improved speech model that is dynamically generated for aparticular user and takes advantage of the information contained in theuser local communication network. The local communication network may beperiodically or continuously updated or pruned as calling patternsevolve over time.

FIG. 5 illustrates an example method embodiment of the invention. Asshown, the method uses the local communication network to generate aspeech recognition model. An exemplary method includes retrieving for anindividual a list of numbers in a calling history (502), identifying alocal neighborhood associated with each member in the calling history(504), truncating the local neighborhood associated with each numberbased on the at least one parameter (506), retrieving a localcommunication networked associated with each number in the callinghistory and each number in the local neighborhood (508) and creating alanguage model for the individual based on the retrieved localcommunication network.

As noted above, the “individual” discussed above may also be just adevice, group of business, a family or any other grouping which may bedefined as the “individual”. The local neighborhood may be defined as alist of numbers which called or are called by the given number. Again,this list may be retrieved based on a calling history of otherindividuals as well as the calling history of the device or theindividual making the calls. Truncating the local neighborhood, usingknown techniques, a method of retaining on the most relevant neighborsof the given number. In other words, there may be outliers in the localneighborhood that have only been called once over the last ten yearswhich may not be as relevant to other numbers that are constantlycalled. Truncating the local neighborhood may occur at a specifiedinterval, such as every week or after every call placed or callreceived, or may occur continuously.

Using the LCN 300, speech recognition models are created. The list ofentities are used to create a pronunciation dictionary and a languagemodel which may be either deterministic (i.e., a list of entities) or astochastic model (i.e., which entity may be spoken with words and itsweight based on its usage and frequency such as the spoken words “callPizza Hut in Summitt”). It is preferred that a universal acoustic modelis used for all users. However, user adaptive acoustic models may alsobe applied.

Once the speech recognition model is created, a user may invoke acommand simply by simply speaking to a device. The LCN-based speechrecognition models then help to narrow down the search space and improvethe recognition performance. It may be calculated that perplexity of thesearch may be reduced from what may be in the order of thousands tobelow 50. Infrequent entities are assigned small language model weight.Another embodiment of the invention is the speech recognition modelitself generated according to the method set forth above as well as thespoken language dialog system which implements an automatic speechrecognition model using the approach above. Furthermore, the languagemodels generated for automatic speech recognition may also include datawhich may be useful for other modules in the spoken dialog system 200.For example, the listing of businesses or entities that are typicallycalled by the user may provide data to aid in generating an appropriateresponse by a dialog manager or a spoken language generation module.Accordingly, the use of the language models generated herein is notlimited just to the automatic speech recognition module.

A benefit of using a local communication network to improve speechinformation search is a drastic reduction of word perplexity whichresults improvement in information search accuracy using speech input.This approach would also further promote more users to depend ontraditional phones and mobile phones for information access usingspeech.

Another aspect of the invention further provides dynamic and continualupdating of the speech models. For example, a phone number that may havebeen called once in the last ten years perhaps may be truncated from anearlier process of developing the speech model, but the truncated datamay be stored in a database in case a user starts to call the phonenumber again. In other words, if the system were to delete the truncateddata, if the user called that number again, then that number may betruncated a second time. However, with the truncation history available,the system may recognize an increased frequency in truncated phonenumbers and then not truncate that particular phone number in a lateriteration and thus, provide an improvement in the speech model for laterrecognition. Furthermore, trends may be identified such as a userswitching from one pizza restaurant to another in which case oncecertain parameters are met, the system may prune out old data in thespeech model based on stale information associated with what telephonenumbers the user is dialing with new information.

Furthermore, a person may have a typical social network of 1,000 peopleor 1,000 phone numbers which some of them are businesses and many areconsumers. The system may know the volume and frequency of how often theperson makes these calls and using that frequency and distributioninformation may enable the system to provide weights to various piecesof data in the grammars. For example, if a user only calls a particulartelephone company once a year then the weight in any grammar is verydifferent for that piece of data then a user that calls their motherevery day. Accordingly, an aspect of this disclosure is to perform ananalysis on the local communication network and the data associated withthat local communication network and weight pieces of information withinthe grammar according to frequency, length of call, and other perhapsancillary information in determining those weights. Thus, the localcommunication network or social network that develops around a user, aphone number, or a device may be utilized to create a more accuratespeech recognition model for such purposes as directory assistanceservices. In another aspect, each business in a local communicationnetwork may be categorized by some industry standard business code andtherefore, the system would know that a particular user is the type ofperson that calls pizza places or if that is the type of person thatgenerally calls home improvement places or financial institutions and soon. The system can then build a profile of each number in that sensewhich would enable a way for the system to create a similarity scorebetween numbers and learn about qualitative factors of individual phonenumbers.

Another aspect would be to utilize deeper information within the localcommunication network to aid in processing speech. In this example,assume that a user has never called Sam's Pizzeria in Summitt, N.J. butone of their friends has. In other words, other people that are in theuser's social network have called and have communicated with businessesthat may also become part of the speech vocabulary of the user. In thisregard, the steps of retrieving local neighborhood information and localcommunication network information may extend wherein phone numbers maybe identified as businesses or friends of family within the localneighborhood. For example, the system may identify ten numbers in thelocal neighborhood of which four are to family members or to friends. Anapproach may be employed wherein the phone numbers called by the familyand friends may also be explored and retrieved as well as associateddata, business names and so forth of those numbers. In this regard, thecommon practice of a friend recommending a restaurant such as Sam'sPizzeria in Summitt may be easily exploited according to the principlesof the invention. In other words, if a user who has not been to Sam'sPizzeria calls up and forgets that name of “Sam's” but desires to go tothat pizzeria in Summitt, the user may say “I would like to go to thatpizzeria that John recommended in Summitt”. Because the localcommunication network is expanded into phone numbers that John called(whose number is in the local neighborhood of the individual) therecognizer may be able to draw out that particular pizza restaurant thatJohn calls and thus would have likely recommended to his friend.

In one aspect, the system gauges how to the extent that they broaden thedatabase which can become too large. In other words, as the system wereto expand out to new phone numbers called by and which receive calls inthe local neighborhood, there may be different parameters for truncatingor pruning out numbers which are in deeper layers of the localcommunication network rather than in higher layers such that thedatabase only gathers the most utilized data.

It is preferable that unnecessary data is truncated. Roughly 90% ofcommunication that an individual does is with the top 8 to 10 people onthe list. So in one aspect, the system may truncate almost everybodyelse in terms of making the complexity of the problem much easier. Inthis regard, the system would only focus on the most relevant people andonly include that data in the speech recognition system rather thanincorporating hundreds of people into each person's model. Onepreferable method of truncating data is to use a proximity measurer.This would measure proximity between any two people. As is known in theart, this would be a simple measure of how many total minutes peopletalked to each other. Another parameter may be how many people you havein common with another person or how many businesses you call. In otherwords, one person may not talk to another person often but they may havethree or four friends in common which may increase their proximitymeasure in the analysis. This proximity measure may be used in thetruncation or pruning process.

Another concept in this regard is assigning a parameter or value to eachperson. For example, every month there may be a group of people anindividual meets with and therefore typically makes phone calls that areshort but are made on a regular basis, for example to coordinate themeeting. Without further data, a value assigned to this individual interms of a measure of proximity may be reduced because the length oftime for each phone call is short. However, since it is called on afairly regular basis the system may allow for a dynamic modification ofa model after this analysis wherein, perhaps, a week before the meetingthe language model gets updated or selected to accommodate some of thedata that otherwise wouldn't make this cut. In other words, the systemmay, knowing the timing of a calling history, preemptively select theappropriate language model to anticipate particular phone calls which anindividual is likely to make. Another example of this is if anindividual orders pizza from the same pizza restaurant every Fridaynight, the system may not employ such a language model Monday throughThursday, but deploy that particular speech recognition model on Fridayin order to improve the increased understanding in anticipation of thepizza order.

An entity operating such a spoken dialog system may update dynamicallyvarious communities or user signatures on a regular basis such asBailey. Such systems do realize that some relationships are dynamic,wherein a user makes one call for two hours every two or three monthsand in other relationships the user may talk for five minutes once aweek. The parameterization of the social network may be set up ormodified in such a way such that both of these types of situations wouldremain in the system and neither would get truncated out.

Embodiments within the scope of the present invention may also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to carryor store desired program code means in the form of computer-executableinstructions or data structures. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or combination thereof) to a computer, the computerproperly views the connection as a computer-readable medium. Thus, anysuch connection is properly termed a computer-readable medium.Combinations of the above should also be included within the scope ofthe computer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,objects, components, and data structures, etc. that perform particulartasks or implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps. Program modules may also comprise any tangible computer-readablemedium in connection with the various hardware computer componentsdisclosed herein, when operating to perform a particular function basedon the instructions of the program contained in the medium.

Those of skill in the art will appreciate that other embodiments of theinvention may be practiced in network computing environments with manytypes of computer system configurations, including personal computers,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. Embodiments may also be practiced indistributed computing environments where tasks are performed by localand remote processing devices that are linked (either by hardwiredlinks, wireless links, or by a combination thereof) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

Although the above description may contain specific details, they shouldnot be construed as limiting the claims in any way. Other configurationsof the described embodiments of the invention are part of the scope ofthis invention. Accordingly, the appended claims and their legalequivalents should only define the invention, rather than any specificexamples given.

1. (canceled)
 2. A method comprising: receiving a calling historyassociated with an individual; identifying data of a social networkassociated with each number in the calling history; refining the data ofthe social network based on at least one parameter, to yield refineddata of the social network; and creating, via a processor, a languagemodel for the individual based on the refined data of the socialnetwork.
 3. The method of claim 2, wherein the data of the socialnetwork further comprises at least one of data corresponding to anentity, a business name, and a person's name.
 4. The method of claim 2,further comprising implementing the language model in a spoken dialogsystem.
 5. The method of claim 2, further comprising using the refineddata of the social network to create a punctuation dictionary.
 6. Themethod of claim 2, wherein the language model is one of a deterministicmodel and a stochastic model.
 7. The method of claim 2, furthercomprising dynamically modifying the language model based on additionaldata.
 8. The method of claim 2, further comprising implementing thelanguage model in a spoken dialog system at a time based on a history ofthe individual and to anticipate user input.
 9. A non-transitorycomputer-readable storage module containing instructions which, whenexecuted by a computing device, cause the computing device to generate aspeech recognition model, the instructions comprising: retrieving a listof numbers in a calling history associated with an individual;identifying data of a social network associated with each number in thelist of numbers; refining the data of the social network based on atleast one parameter, to yield refined data of the social network; andcreating, via a processor, a language model for the individual based onthe refined data of the social network.
 10. The non-transitorycomputer-readable storage module of claim 9, wherein the data of thesocial network further comprises at least one of data corresponding toan entity, a business name, and a person's name.
 11. The non-transitorycomputer-readable storage module of claim 9, wherein the method used tocreate the speech recognition further comprises using the refined dataof the social network to create a punctuation dictionary.
 12. Thenon-transitory computer-readable storage module of claim 9, wherein thelanguage model is one of a deterministic model and a stochastic model.13. The non-transitory computer-readable storage module of claim 9,further comprising dynamically modifying the language model based onadditional data.
 14. The non-transitory computer-readable storage moduleof claim 9, further comprising implementing the language model in aspoken dialog system at a time based on a history of the individual andto anticipate user input.
 15. A spoken dialog system having a speechmodel, the spoken dialog system comprising: a processor; a first moduleconfigured to control the processor to receive speech from a user; and asecond module configured to control the processor to perform speechrecognition on the speech using the speech model, wherein the speechmodel is generated by a method comprising: retrieving for an individuala calling list associated with the individual; identifying data of asocial network associated with each number in the calling history;refining the data of the social network based on at least one parameter,to yield refined data of the social network; and creating, via aprocessor, a language model for the individual based on the refined dataof the social network.
 16. The spoken dialog system of claim 15, whereinthe data of the social network comprises at least one of datacorresponding to an entity, a business name, and a person's name. 17.The spoken dialog system of claim 15, wherein the speech model isgenerated by a method further comprising using the refined data of thesocial network to create a punctuation dictionary.
 18. The spoken dialogsystem of claim 15, wherein the language model is one of a deterministicmodel and a stochastic model.
 19. The spoken dialog system of claim 15,wherein the speech model is generated by a method further comprisingdynamically modifying the language model based on additional data. 20.The spoken dialog system of claim 15, wherein the speech model isgenerated by a method further comprising implementing the language modelin the spoken dialog system at a time based on a history of theindividual and to anticipate user input.
 21. The spoken dialog system ofclaim 15, wherein the speech model is generated by a method furthercomprising implementing the language model in a spoken dialog system.